Compute · Storage · Network · Observability · Security
Krambu AI Cloud
A full-stack GPU cloud for teams building with AI. Compute, storage, networking, observability, and security are engineered as one platform — so training runs finish, inference stays fast, and nothing waits on the layer beneath it.
GPU Fabric
3.2 Tb/s
Ethernet
800 Gb/s
Uptime SLA
99.98%
Support
24/7/365
The Platform
Every Layer Tuned for AI
Most clouds bolt accelerators onto general-purpose infrastructure. We specify from the workload down — the fabric, the file system, the scheduler, and the telemetry are all built for jobs that are bursty, checkpoint-heavy, and intolerant of jitter.
Bare metal, virtual machines, and containers on current-generation NVIDIA accelerators.
StorageParallel file systems, NVMe block, and object storage sized in throughput, not just terabytes.
NetworkingNon-blocking InfiniBand and RoCE fabric with private interconnects between facilities.
ObservabilityGPU, fabric, and storage telemetry in one view, with alerts before a run goes sideways.
SecurityTenant isolation, encryption, and immutable audit trails around your weights and data.
Compute
Compute That Scales With the Model
From a single interactive GPU for prototyping to multi-thousand-GPU superclusters for frontier training runs. Provision bare metal, virtual machines, or containers from one control plane — and change shape when the workload does.
Bare Metal
Single-tenant nodes with nothing between your code and the silicon. Direct access to NVLink, RDMA, and local NVMe for the runs where every percent of utilisation counts.
- Zero Hypervisor Overhead
- Dedicated NVMe & NICs
- Root Hardware Access
Virtual Machines
Isolated instances with GPU passthrough, sized from one accelerator to a full node. Snapshots, custom images, and flexible CPU-to-GPU ratios for shared research environments.
- 1 — 8 GPUs per Instance
- Snapshots & Custom Images
- Flexible CPU & Memory
Containers & K8s
Managed orchestration with GPU-aware scheduling, gang scheduling for distributed jobs, and autoscaling worker pools. Bring your own images or start from our AI base stack.
- GPU-Aware Scheduling
- Multi-Node Job Launching
- Autoscaling Worker Pools
Accelerator Platforms
Current-generation NVIDIA systems, deployed in liquid- and air-cooled configurations across Krambu AI factories.
NVIDIA HGX™ GB300
Grace-Blackwell superchips for the largest runs — mission-critical infrastructure that scales to tens of thousands of accelerators without falling off a cliff.
- GB300 Superchips pairing Grace CPUs with Blackwell Ultra GPUs
- 1.8 TB/s of GPU-to-GPU bandwidth inside the domain
- Scales to tens of thousands of GPUs over Quantum InfiniBand
- Built for trillion-parameter generative models
NVIDIA HGX™ B300
Eight Blackwell Ultra GPUs per node on fifth-generation NVLink. The workhorse for teams scaling large language models, recommenders, and multimodal stacks.
- 72 petaFLOPS training, 144 petaFLOPS inference per system
- 8× Blackwell Ultra GPUs on 5th-gen NVIDIA NVLink
- 3× the training and 15× the inference of the prior generation
- Liquid-cooled 4U or air-cooled 8U node options
NVIDIA HGX™ H100
The platform most production fine-tuning and inference already targets. Broad framework support, predictable performance, and capacity available now.
- 8× H100 Tensor Core GPUs with 640 GB of HBM3
- Up to 32 petaFLOPS of FP8 precision per system
- 900 GB/s bidirectional GPU-to-GPU bandwidth
- 2 TB system memory in an 8U rackmount chassis
General-Purpose CPU Compute
Not everything belongs on a GPU. Run data preparation, tokenisation, feature stores, orchestration, and inference gateways on high-core-count CPU instances with large memory footprints — virtual or containerised, on the same platform and the same bill, at rates that don’t punish you for staying off the accelerators.
Storage
Storage Engineered for AI
A GPU waiting on data is a GPU you are paying for and not using. Krambu storage is specified in throughput, not just capacity — petabytes of high-performance namespace reachable from every node over RDMA, with no ingress or egress charges attached.
Parallel File System
A petabyte-scale POSIX namespace mounted on every node in the cluster, built for thousands of workers hammering the same shards at once. Custom-sized to your dataset and your read pattern.
- Petabyte Namespaces
- Shared Across Every Node
- 500 GB/s Aggregate Read
GPUDirect Storage
NVIDIA GPUDirect Storage moves data from NVMe straight into GPU memory over RDMA, skipping the CPU bounce buffer entirely. It is the difference between feeding a node and starving it.
- 40+ GB/s Direct to GPU Memory
- RDMA From Every Machine
- No CPU Bounce Buffer
NVMe Block Volumes
Low-latency block devices for boot volumes, databases, and vector indexes. Snapshot and clone in seconds, attach to bare metal or virtual instances alike.
Object Storage
S3-compatible buckets for datasets, checkpoints, and model artefacts. Works with the tooling and data loaders you already use — no rewrites, no transfer bill.
Cache Tiers
System RAM and local NVMe are used as caching tiers, delivering up to 10× faster repeat reads for epochs that revisit the same data — without you managing a thing.
Checkpointing Without the Stall
Write throughput of up to 20 GB/s per node means terabyte-scale checkpoints land in seconds rather than minutes. On a long run across hundreds of nodes, that difference compounds into days of recovered GPU time — and a far shorter path back after a failure.
Networking
A Fabric That Doesn’t Block
Past a few dozen GPUs, distributed training stops being a compute problem and becomes a networking one. Krambu runs rail-optimised, non-blocking fabric to every accelerator, separate high-speed front-end Ethernet, and fault-tolerant capacity that scales horizontally across facilities.
GPU Fabric
Up to 3.2 Tb/s per node over NVIDIA Quantum InfiniBand or Spectrum-X Ethernet, in rail-optimised non-blocking topologies. RDMA and GPUDirect end to end, so collectives run at line rate.
- InfiniBand or RoCEv2
- Non-Blocking Topology
- RDMA End to End
Front-End Ethernet
Up to 800 Gb/s per node for ingest, serving, and storage traffic, kept off the training fabric. Horizontally scalable and fault-tolerant, with carrier-diverse paths out of every facility.
- 800 Gb/s Per Node
- Carrier-Diverse Fiber Paths
- Fault-Tolerant by Design
Private Connectivity
Isolated VLANs and private interconnects between your instances and facilities, direct connections into your own network, and always-on DDoS mitigation at the edge.
- Private VLANs & Interconnect
- Direct Connect to Your Network
- Always-On DDoS Mitigation
Observability
See Every Layer of the Stack
Real-time visibility across bare metal, virtual, and containerised workloads in a single pane of glass. Dashboards work out of the box, alerts are yours to shape, and every metric is available to your own tooling.
Catch It Early
Alerts on GPU thermals, ECC errors, memory pressure, and inference latency fire before a node degrades — so a bad card is replaced during a checkpoint window rather than halfway through an epoch.
Correlate End to End
GPU metrics are joined with logs, traces, fabric counters, storage throughput, and job state. A slow step maps back to the node, link, or volume actually responsible instead of a guess.
Every Architecture
Full telemetry across Hopper and Blackwell generations, NVLink domains, and CPU fleets — with consistent metric names, so your dashboards survive a hardware migration.
Your Tools, Too
Watch live utilisation across cloud, colocated, and on-premises footprints, and export everything to Prometheus, Grafana, or whatever your platform team already runs.
Storage Telemetry
Read and write throughput, IOPS, queue depth, and cache hit rates — the numbers that explain why a data loader is starving your GPUs.
Fabric Telemetry
Bandwidth, latency, packet loss, and link health across InfiniBand and RoCEv2 — including the congestion signals that quietly slow collectives.
Security Telemetry
Access logs, anomaly detection, and encryption status monitored continuously, so integrity checks never come at the cost of throughput.
Security
Secure by Design, Not by Add-On
Your weights and your training data are the most valuable things on the cluster. We isolate, encrypt, and audit them at every layer — meeting strict infosec requirements without the rigidity that stops a small team from shipping.
Isolation & Segmentation
Dedicated hardware, private VLANs, and granular network policy keep tenants and environments apart. No shared accelerators, no noisy neighbours on your fabric.
Encryption & Data Control
Encryption in transit and at rest with granular key control, plus explicit answers on where data lives, how long it stays, and what happens when you delete it.
Audit & SIEM Access
Detailed logs, event tracing, and immutable audit trails, streamable into your SIEM. Track, verify, and validate every action taken inside your environment.
Detection & Response
Real-time intrusion detection, anomaly alerting, and automated remediation — backed by an on-call team that works in the same buildings as your hardware.
Physical Security
Your workloads run in facilities Krambu operates: biometric access control, continuous surveillance, escorted entry, and staff on site around the clock.
Compliance & Provenance
Built for strict data-privacy and provenance requirements, with the controls and evidence your auditors ask for — and enough flexibility that startups aren’t slowed down by them.
Deploy & Operate
Running It Should Be the Easy Part
Developer-First Tooling
Provision and manage bare metal and virtual instances from a documented API and CLI. Anything you can do in the console is scriptable, so environments stay reproducible.
Multi-Facility Deployment
Place workloads across Krambu AI factories for capacity, redundancy, or data residency, connected by private high-speed links between sites.
Expert Support, 24/7/365
AI infrastructure engineers, not a ticket queue. You build a relationship with the same people who rack, cool, and cable the hardware your jobs run on.
Built on Krambu Infrastructure
Every instance runs on hardware in facilities we own and operate. No reselling, no middlemen, and no queue behind someone else’s customers.
- 100% renewable hydroelectric power across all locations
- Direct liquid cooling for GPU-dense deployments
- Carrier-neutral connectivity with diverse fiber paths
- 24/7 on-site engineering and support staff
- Multiple US AI factories with sub-millisecond inter-facility latency
The cloud platform runs on the same infrastructure behind our colocation and cluster customers. You get the same power density, cooling capacity, and network performance — without owning the hardware or the headcount to look after it.
Need a shape we haven’t listed — a specific GPU count, a storage tier sized to your dataset, a hybrid of cloud and colocation? We design custom configurations every week. Tell us what the workload looks like and we’ll build to it.
Ready to Scale Your Infrastructure?
Whether you need GPU clusters, colocation, or custom server solutions — our team is ready to help design the perfect infrastructure for your needs.